Drones, Volume 8, Issue 4 (April 2024) – 51 articles

In the evolving landscape of autonomous systems, the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has emerged as a promising solution for improving the localization accuracy and operational efficiency for diverse applications. This study introduces an Information Consensus Filter (ICF)-based decentralized control system for UAVs, incorporating the Control Barrier Function–Control Lyapunov Function (CBF–CLF) strategy aimed at enhancing operational safety and efficiency. At the core of our approach lies an ICF-based decentralized control algorithm that allows UAVs to autonomously adjust their flight controls in real time based on inter-UAV communication. This facilitates cohesive movement operation, significantly improving the system resilience and adaptability. Meanwhile, the UAV is equipped with a visual recognition system designed for tracking and locating the UGV. According to the experiments proposed in the paper, the precision of this visual recognition system correlates significantly with the operational distance. The proposed CBF–CLF strategy dynamically adjusts the control inputs to maintain safe distances between the UAV and UGV, thereby enhancing the accuracy of the visual system. The effectiveness and robustness of the proposed system are demonstrated through extensive simulations and experiments, highlighting its potential for widespread application in UAV operational domains. Full article

The tight formation of unmanned aerial vehicles (UAVs) provides numerous advantages in practical applications, increasing not only their range but also their efficiency during missions. However, the wingman aerodynamics are affected by the tail vortices generated by the leading aircraft in a tight formation, resulting in unpredictable interference. In this study, a mathematical model of wake vortex was developed, and the aerodynamic characteristics of a tight formation were simulated using Xflow software. A robust control method for tight formations was constructed, in which the disturbance is first estimated with an extended state observer, and then a sliding mode controller (SMC) was designed, enabling the wingman to accurately track the position under conditions of wake vortex from the leading aircraft. The stability of the designed controller was confirmed. Finally, the controller was simulated and verified in mathematical simulation and semi-physical simulation platforms, and the experimental results showed that the controller has high tight formation accuracy and is robust. Full article

Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning. Full article

This paper introduces a quadrotor trajectory tracking controller comprising a steady-state optimal position controller with a normed input saturation and modular integrative action coupled with a backstepping attitude controller. First, the translational and rotational dynamical models are designed in the body-fixed frame to avoid external rotations and are partitioned into an underactuated position system and a quaternion-based attitude system. Secondly, a controller is designed separately for each subsystem, namely, (i) the position controller synthesis is derived from the Maximum Principle, Lyapunov, and linear quadratic regulator (LQR) theory, ensuring the global exponential stability and steady-state optimality of the controller within the linear region, and global asymptotic stability is guaranteed for the saturation region when coupled with any local exponential stable attitude controller, and (ii) the attitude system, with the quaternion angles and the angular velocity as the controlled variables, is designed in the error space through the backstepping technique, which renders the overall system, position, and attitude, with desirable closed-loop properties that are almost global. The overall stability of the system is achieved through the propagation of the position interconnection term to the attitude system. To enhance the robustness of the tracking system, integrative action is devised for both position and attitude, with emphasis on the modular approach for the integrative action on the position controller. The proposed method is experimentally validated on board an off-the-shelf quadrotor to assess the resulting performance. Full article

This work is inscribed into a more comprehensive project related to the architectural requalification and restoration of Frinco Castle, one of the most significant fortified medieval structures in the Monferrato area (province of Asti, Italy), that experienced a structural collapse. In particular, this manuscript focuses on data fusion of multi-sensor acquisitions of metric surveys for 3D documenting this structural-risky building. The structural collapse made the entire south front fragile. The metric survey was performed by using terrestrial and aerial sensors to reach every area of the building. Topographically oriented Terrestrial Laser Scans (TLS) data were collected for the exterior and interior of the building, along with the DJI Zenmuse L1 Airborne Laser Scans (ALS) and Zenmuse P1 Photogrammetric Point Cloud (APC). First, the internal alignment in the TLS data set was verified, followed by the intra-technique alignments, choosing TLS as the reference data set. The point clouds from each sensor were analyzed by computing voxel-based point density and roughness, then segmented, aligned, and fused. 3D acquisitions and segmentation processes were fundamental for having a complete and structured dataset of almost every outdoor and indoor area of the castle. The collected metrics data was the starting point for the modeling phase to prepare 2D and 3D outputs fundamental for the restoration process. Full article

A UAV infrared target detection model ITD-YOLOv8 based on YOLOv8 is proposed to address the issues of model missed and false detections caused by complex ground background and uneven target scale in UAV aerial infrared image target detection, as well as high computational complexity. Firstly, an improved YOLOv8 backbone feature extraction network is designed based on the lightweight network GhostHGNetV2. It can effectively capture target feature information at different scales, improving target detection accuracy in complex environments while remaining lightweight. Secondly, the VoVGSCSP improves model perceptual abilities by referencing global contextual information and multiscale features to enhance neck structure. At the same time, a lightweight convolutional operation called AXConv is introduced to replace the regular convolutional module. Replacing traditional fixed-size convolution kernels with convolution kernels of different sizes effectively reduces the complexity of the model. Then, to further optimize the model and reduce missed and false detections during object detection, the CoordAtt attention mechanism is introduced in the neck of the model to weight the channel dimensions of the feature map, allowing the network to pay more attention to the important feature information, thereby improving the accuracy and robustness of object detection. Finally, the implementation of XIoU as a loss function for boundary boxes enhances the precision of target localization. The experimental findings demonstrate that ITD-YOLOv8, in comparison to YOLOv8n, effectively reduces the rate of missed and false detections for detecting multi-scale small targets in complex backgrounds. Additionally, it achieves a 41.9% reduction in model parameters and a 25.9% decrease in floating-point operations. Moreover, the mean accuracy (mAP) attains an impressive 93.5%, thereby confirming the model’s applicability for infrared target detection on unmanned aerial vehicles (UAVs). Full article

Aerial images record the dynamic Earth terrain, reflecting changes in land cover patterns caused by natural processes and human activities. Nonetheless, prevailing aerial image classification methodologies predominantly function within a closed-set framework, thereby encountering challenges when confronted with the identification of newly emerging scenes. To address this, this paper explores an aerial image recognition scenario in which a dataset comprises both labeled and unlabeled aerial images, intending to classify all images within the unlabeled subset, termed Generalized Category Discovery (GCD). It is noteworthy that the unlabeled images may pertain to labeled classes or represent novel classes. Specifically, we first develop a contrastive learning framework drawing upon the cutting-edge algorithms in GCD. Based on the multi-object characteristics of aerial images, we then propose a slot attention-based GCD training process (Slot-GCD) that contrasts learning at both the object and image levels. It decouples multiple local object features from feature maps using slots and then reconstructs the overall semantic feature of the image based on slot confidence scores and the feature map. Finally, these object-level and image-level features are input into the contrastive learning module to enable the model to learn more precise image semantic features. Comprehensive evaluations across three public aerial image datasets highlight the superiority of our approach over state-of-the-art methods. Particularly, Slot-GCD achieves a recognition accuracy of 91.5% for known old classes and 81.9% for unknown novel class data on the AID dataset. Full article

A technology to search for victims in disaster areas by localizing human-related sound sources, such as voices and emergency whistles, using a drone-embedded microphone array was researched. One of the challenges is the development of sound source localization methods. Such a sound-based search method requires a high resolution, a high tolerance for quickly changing dynamic ego-noise, a large search range, high real-time performance, and high versatility. In this paper, we propose a novel sound source localization method based on multiple signal classification for victim search using a drone-embedded microphone array to satisfy these requirements. In the proposed method, the ego-noise and target sound components are extracted using the histogram information of the three-dimensional spatial spectrum (azimuth, elevation, and frequency) at the current time, and they are separated using continuity. The direction of arrival of the target sound is estimated from the separated target sound component. Since this method is processed with only simple calculations and does not use previous information, all requirements can be satisfied simultaneously. Evaluation experiments using recorded sound in a real outdoor environment show that the localization performance of the proposed method was higher than that of the existing and previously proposed methods, indicating the usefulness of the proposed method. Full article

Unmanned helicopters are always subject to various external disturbances and constraints when performing tasks. In this paper, an extended state observer-based command-filtered safe tracking control scheme is investigated for an unmanned helicopter under time-varying path constraints and disturbances. To restrict the position states within the real-time safe flight boundaries, a safe reference path is regulated using the safe protection algorithm. The ESO is utilized to handle the unknown external disturbances. Moreover, the command filter technique is combined with the backstepping approach and twice inverse solution for the nonlinear unmanned helicopter system. According to the Lyapunov stability analysis, the safety and the tracking performance of the helicopter can be proved, and the availability of the safe tracking controller can also be illustrated by numerical simulations. Full article

A UAV-assisted space–air–ground integrated network (SAGIN) can provide communication services for remote areas and disaster-stricken regions. However, the increasing types and numbers of ground terminals (GTs) have led to the explosive growth of communication data volume, which is far from meeting the communication needs of ground users. We propose a mobile edge network model that consists of three tiers: satellites, UAVs, and GTs. In this model, UAVs and satellites deploy edge servers to deliver services to GTs. GTs with limited computing capabilities can upload computation tasks to UAVs or satellites for processing. Specifically, we optimize association control, bandwidth allocation, computation task allocation, caching decisions, and the UAV’s position to minimize task latency. However, the proposed joint optimization problem is complex, and it is difficult to solve. Hence, we utilize Block Coordinate Descent (BCD) and introduce auxiliary variables to decompose the original problem into different subproblems. These subproblems are then solved using the McCormick envelope theory, the Successive Convex Approximation (SCA) method, and convex optimization techniques. The simulation results extensively illustrate that the proposed solution dramatically decreases the overall latency when compared with alternative benchmark schemes. Full article

In this paper, we propose a three-layer distributed simulation network architecture, which consists of a distributed virtual simulation network, a perception and control subnetwork, and a cooperative communication service network. The simulation architecture runs on a distributed platform, which can provide unique virtual scenarios and multiple simulation services for the verification of basic perception, control, and planning algorithms of a single-robot system and can verify the distributed collaboration algorithms of heterogeneous multirobot systems. Further, we design simulation experimental scenarios for classic heterogeneous robotic systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Through the analysis of experimental measurement data, we draw several important conclusions: firstly, the replication time characteristics and update frequency characteristics of entity synchronization in our system indicate that the replication time of entity synchronization in our system is relatively short, and the update frequency can meet the needs of multirobot collaboration and ensure the real-time use and accuracy of the system; secondly, we analyze the bandwidth usage of data frames in the whole session and observe that the server side occupies almost half of the data throughput during the whole session, which indicates that the allocation and utilization of data transmission in our system is reasonable; and finally, we construct a bandwidth estimation surface model to estimate the bandwidth requirements of the current model when scaling the server-side scale and synchronization-state scale, which provides an important reference for better planning and optimizing of the resource allocation and performance of the system. Based on this distributed simulation framework, future research will improve the key technical details, including further refining the coupling object dynamic model update method to support the simulation theory of the coupling relationship between system objects, studying the impact of spatiotemporal consistency of distributed systems on multirobot control and decision making, and in-depth research on the impact of collaborative frameworks combined with multirobot systems for specific tasks. Full article

Multi-unmanned systems have demonstrated significant applications across various fields under complex or extreme operating environments. In order to make such systems highly efficient and reliable, cooperative decision-making methods have been utilized as a critical technology for successful future applications. However, current multi-agent decision-making algorithms pose many challenges, including difficulties understanding human decision processes, poor time efficiency, and reduced interpretability. Thus, a real-time online collaborative decision-making model simulating human cognition is presented in this paper to solve those problems under unknown, complex, and dynamic environments. The provided model based on the Soar cognitive architecture aims to establish domain knowledge and simulate the process of human cooperation and adversarial cognition, fostering an understanding of the environment and tasks to generate real-time adversarial decisions for multi-unmanned systems. This paper devised intricate forest environments to evaluate the collaborative capabilities of agents and their proficiency in implementing various tactical strategies while assessing the effectiveness, reliability, and real-time action of the proposed model. The results reveal significant advantages for the agents in adversarial experiments, demonstrating strong capabilities in understanding the environment and collaborating effectively. Additionally, decision-making occurs in milliseconds, with time consumption decreasing as experience accumulates, mirroring the growth pattern of human decision-making. Full article

The application of computer vision in conjunction with GPS is essential for autonomous wind turbine inspection, particularly when the drone navigates through a wind farm to detect the turbine of interest. Although drones for such inspections use GPS, our study only focuses on the computer vision aspect of navigation that can be combined with GPS information for better navigation in a wind farm. Here, we employ an affordable, non-GPS-equipped drone within an indoor setting to serve educational needs, enhancing its accessibility. To address navigation without GPS, our solution leverages visual data captured by the drone’s front-facing and bottom-facing cameras. We utilize Hough transform, object detection, and QR codes to control drone positioning and calibration. This approach facilitates accurate navigation in a traveling salesman experiment, where the drone visits each wind turbine and returns to a designated launching point without relying on GPS. To perform experiments and investigate the performance of the proposed computer vision technique, the DJI Tello EDU drone and pedestal fans are used to represent commercial drones and wind turbines, respectively. Our detailed and timely experiments demonstrate the effectiveness of computer vision-based path planning in guiding the drone through a small-scale surrogate wind farm, ensuring energy-efficient paths, collision avoidance, and real-time adaptability. Although our efforts do not replicate the actual scenario of wind turbine inspection using drone technology, they provide valuable educational contributions for those willing to work in this area and educational institutions who are seeking to integrate projects like this into their courses, such as autonomous systems. Full article

This paper presents a systematic literature review investigating the integration of lightweight blockchain and fog computing technologies to enhance the security and operational efficiency of drones. With a focus on critical applications such as military surveillance and emergency response, this review examines how the combination of blockchain’s secure, decentralized ledger and fog computing’s low-latency, localized data processing can address the unique challenges of drone operations. By compiling and analyzing current research, this study highlights innovative approaches and solutions that leverage these technologies to improve data integrity, reduce communication latency, and facilitate real-time decision-making in drone missions. Our findings underscore the significant potential of this technological integration to advance the capabilities and reliability of drones in high-stakes scenarios. Full article

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations. Full article

Artemisia frigida, as an important indicator species of grassland degradation, holds significant guidance significance for understanding grassland degradation status and conducting grassland restoration. Therefore, conducting rapid surveys and monitoring it is crucial. In this study, to address the issue of insufficient identification accuracy due to the large density and small size of Artemisia frigida in UAV images, we improved the YOLOv7 object detection algorithm to enhance the performance of the YOLOv7 model in Artemisia frigida detection. We applied the improved model to the detection of Artemisia frigida across the entire experimental area, achieving spatial mapping of Artemisia frigida distribution. The results indicate: In comparison across different models, the improved YOLOv7 + Biformer + wise-iou model exhibited the most notable enhancement in precision metrics compared to the original YOLOv7, showing a 6% increase. The mean average precision at intersection over union (IoU) threshold of 0.5 ([email protected]) also increased by 3%. In terms of inference speed, it ranked second among the four models, only trailing behind YOLOv7 + biformer. The YOLOv7 + biformer + wise-iou model achieved an overall detection precision of 96% and a recall of 94% across 10 plots. The model demonstrated superior overall detection performance. The enhanced YOLOv7 exhibited superior performance in Artemisia frigida detection, meeting the need for rapid mapping of Artemisia frigida distribution based on UAV images. This improvement is expected to contribute to enhancing the efficiency of UAV-based surveys and monitoring of grassland degradation. These findings emphasize the effectiveness of the improved YOLOv7 + Biformer + wise-iou model in enhancing precision metrics, overall detection performance, and its applicability to efficiently map the distribution of Artemisia frigida in UAV imagery for grassland degradation surveys and monitoring. Full article

The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, complex background and other factors in the mining area, it is impossible to accurately identify and detect the landslide in the mining area. It is necessary to select an efficient detection model to detect it. In this paper, aiming at the problem of landslide identification in mining area, the remote sensing image of mining area is obtained by unmanned aerial vehicle (UAV), and the landslide data set of mining area is constructed by data enhancement method. An improved YOLOv8 algorithm is proposed. By adding a mixed attention mechanism in the channel and spatial dimensions, the detection accuracy of the model for mining landslide is improved, and the monitoring of landslide changes in the mining area is successfully completed. At the same time, an algorithm for locating the landslide position is proposed. Through this algorithm, the detected landslide pixel coordinates can be converted into geodetic coordinates. The results show that the improved YOLOv8 algorithm proposed in this paper has a recognition accuracy of 93.10% for mining area landslides. Compared with the [email protected] of the original YOLOv8 algorithm and YOLOv5 algorithm, the improved YOLOv8 algorithm has an increase of 4.2% and 5.1%. This study has realized the monitoring and positioning of the landslide in the mining area, which can provide the necessary data support for the ecological restoration on mining area. Full article

The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as efficient temporary aerial base stations and enhance communication quality in instances where terrestrial base stations are incapacitated. Trajectory planning and resource allocation of UAVs continue to be vital techniques, while a relatively limited number of algorithms account for the dynamics of ground users. This paper focuses on emergency communication scenarios such as earthquakes, proposing an innovative path planning and resource allocation algorithm. The algorithm leverages a multi-stage subtask iteration approach, inspired by the block coordinate descent technique, to address the challenges presented in such critical environments. In this study, we establish an air-to-ground communication model, subsequently devising a strategy for user dynamics. This is followed by the introduction of a joint scheduling process for path and resource allocation, named ISATR (iterative scheduling algorithm of trajectory and resource). This process encompasses highly interdependent decision variables, such as location, bandwidth, and power resources. For mobile ground users, we employ the cellular automata (CA) method to forecast the evacuation trajectory. This algorithm successfully maintains data communication in the emergency-stricken area and enhances the communication quality through bandwidth division and power control which varies with time. The effectiveness of our algorithm is validated by evaluating the average throughput with different parameters in various simulation conditions and by using several heuristic methods as a contrast. Full article

The operational regime of Unmanned Aerial Vehicles (UAVs) is distinguished by the dominance of laminar flow and the flow field is characterized by the appearance of Laminar Separation Bubbles (LSBs). Ice accretion on the leading side of the airfoil leads to the formation of an Ice-induced Separation Bubble (ISB). These separation bubbles have a considerable influence on the pressure, heat flux, and shear stress distribution on the surface of airfoils and can affect the prediction of aerodynamic coefficients. Therefore, it is necessary to capture these separation bubbles in the numerical simulations. Previous studies have shown that these bubbles can be modeled successfully using the Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) but are computationally costly. Also, for numerical modeling of ice accretion, the flow field needs to be recomputed at specific intervals, thus making LES and DNS unsuitable for ice accretion simulations. Thus, it is necessary to come up with a Reynolds-Averaged Navier–Stokes (RANS) equation-based model that can predict the LSBs and ISBs as accurately as possible. Numerical studies were performed to assess the fidelity of various RANS turbulence models in predicting LSBs and ISBs. The findings are compared with the experimental and LES data available in the literature. The structure of these bubbles is only studied from a pressure coefficient perspective, so an attempt is made in these studies to explain it using the skin friction coefficient distribution. The results indicate the importance of the use of transition-based models when dealing with low-Reynolds-number applications that involve LSB. ISB can be predicted by conventional RANS models but are subjected to high levels of uncertainty. Possible recommendations were made with respect to turbulence models when dealing with flows involving LSBs and ISBs, especially for ice accretion simulations. Full article

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions. Full article

At present, all the flight controllers of agricultural UAVs cannot accurately and quickly control the influencing factors of the UAV coupled rotor airflow backward tilt angle during the application process. To solve the above problem, a Rotor Airflow Backward Tilt Angle (RABTA) controller is established in this paper. The RABTA controller integrates advanced sensor technology with a novel algorithmic approach, utilizing real-time data acquisition and state–space analysis to dynamically adjust the UAV’s rotor airflow, ensuring precise control of the backward tilt angle. The control effect of the traditional flight controller and RABTA controller in the process of pesticide application and the corresponding operation effect are compared and analyzed. The comparison results show that the RABTA controller reduces the control error to less than 1 degree, achieving a 48.3% improvement in the uniformity of the distribution of pesticides droplets across the crop canopy, which means that the UAV field application effect is implemented and the innovation of the UAV field application control mode is realized. Full article

Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%. Full article

With the continuous advancement of drone technology, drones are demonstrating a trend toward autonomy and clustering. The detection of airborne objects from the perspective of drones is critical for addressing threats posed by aerial targets and ensuring the safety of drones in the flight process. Despite the rapid advancements in general object detection technology in recent years, the task of object detection from the unique perspective of drones remains a formidable challenge. In order to tackle this issue, our research presents a novel and efficient mechanism for adjacent frame fusion to enhance the performance of visual object detection in airborne scenarios. The proposed mechanism primarily consists of two modules: a feature alignment fusion module and a background subtraction module. The feature alignment fusion module aims to fuse features from aligned adjacent frames and key frames based on their similarity weights. The background subtraction module is designed to compute the difference between the foreground features extracted from the key frame and the background features obtained from the adjacent frames. This process enables a more effective enhancement of the target features. Given that this method can significantly enhance performance without a substantial increase in parameters and computational complexity, by effectively leveraging the feature information from adjacent frames, we refer to it as an efficient adjacent frame fusion mechanism. Experiments conducted on two challenging datasets demonstrate that the proposed method achieves superior performance compared to existing algorithms. Full article

With the growing prominence of UAV-based low-altitude remote sensing in agriculture, the acquisition and processing of high-quality UAV remote sensing images is paramount. The purpose of this study is to investigate the impact of various parameter settings on image quality and optimize these parameters for UAV operations to enhance efficiency and image quality. The study examined the effects of three parameter settings (exposure time, flight altitudes and forward overlap (OF)) on image quality and assessed images obtained under various conditions using signal-to-noise ratio (SNR) and BRISQUE algorithms. The results indicate that the setting of exposure time during UAV image acquisition directly affects image quality, with shorter exposure times resulting in lower SNR. The optimal exposure times for the RGB and MS cameras have been determined as 0.8 ms to 1.1 ms and 4 ms to 16 ms, respectively. Additionally, the best image quality is observed at flight altitudes between 15 and 35 m. The setting of UAV OF complements exposure time and flight altitude; to ensure the completeness of image acquisition, it is suggested that the flight OF is set to approximately 75% at a flight altitude of 25 m. Finally, the proposed image redundancy removal method has been demonstrated as a feasible approach for reducing image mosaicking time (by 84%) and enhancing the quality of stitched images (by 14%). This research has the potential to reduce flight costs, improve image quality, and significantly enhance agricultural production efficiency. Full article

Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies. Full article

In the expeditiously evolving discipline of autonomous aerial robotics, the efficiency and precision of drone control deliveries have become predominant. Different control strategies for UAV systems have been thoroughly investigated, yet PID controllers still receive significant consideration at various levels in the control loop. Although fractional-order PID controllers (FOPID) have greater flexibility than integer-order PID (IOPID) controllers, they are approached with caution and hesitance. This is due to the fact that FOPID controllers are more computationally intensive to tune, as well as being more challenging to implement accurately in real time. In this paper, we address this problem by developing and implementing a surrogate-based analysis and optimization (SBAO) of a relatively high-order approximation of FOPID controllers. The proposed approach was verified through two case studies; a simulation quadrotor benchmark model for waypoint navigation, and a real-time twin-rotor copter system. The obtained results validated and favored the SBAO approach over other classical heuristic methods for IOPID and FOPID. Full article

Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R² = 0.99, RMSE = 0.09 plants/m²). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R² = 0.76, RMSE = 0.67 plants/m²). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases. Full article

Conservation efforts in cliff habitats pose unique challenges due to their inaccessibility, limiting the study and protection of rare endemic species. This project introduces a novel approach utilizing aerial manipulation through a suspended manipulator attached with a cable under a drone to address these challenges. Unlike existing solutions, the Mamba provides a horizontal reach up to 8 m to approach cliffs while keeping the drone at a safe distance. The system includes a model-based control system relying solely on an inertial measurement unit (IMU), reducing sensor requirements and computing power to minimize overall system mass. This article presents novel contributions such as a double pendulum dynamic modeling approach and the development and evaluation of a precise control system for sampling operations. Indoor and outdoor tests demonstrate the effectiveness of the suspended aerial manipulator in real-world environments allowing the collection of 55 samples from 28 different species. This research signifies a significant step toward enhancing the efficiency and safety of conservation efforts in challenging cliff habitats. Full article

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations. Full article

Fast and accurate pose estimation is essential for the local motion control of robots such as drones. At present, camera-based motion capture (Mocap) systems are mostly used by robots. However, this kind of Mocap system is easily affected by light noise and camera occlusion, and the cost of common commercial Mocap systems is high. To address these challenges, we propose Easy Rocap, a low-cost, open-source robot motion capture system, which can quickly and robustly capture the accurate position and orientation of the robot. Firstly, based on training a real-time object detector, an object-filtering algorithm using class and confidence is designed to eliminate false detections. Secondly, multiple-object tracking (MOT) is applied to maintain the continuity of the trajectories, and the epipolar constraint is applied to multi-view correspondences. Finally, the calibrated multi-view cameras are used to calculate the 3D coordinates of the markers and effectively estimate the 3D pose of the target robot. Our system takes in real-time multi-camera data streams, making it easy to integrate into the robot system. In the simulation scenario experiment, the average position estimation error of the method is less than 0.008 m, and the average orientation error is less than 0.65 degrees. In the real scenario experiment, we compared the localization results of our method with the advanced LiDAR-Inertial Simultaneous Localization and Mapping (SLAM) algorithm. According to the experimental results, SLAM generates drifts during turns, while our method can overcome the drifts and accumulated errors of SLAM, making the trajectory more stable and accurate. In addition, the pose estimation speed of our system can reach 30 Hz. Full article